Light pollution is ubiquitous in much of the developed and developing world, including rural and wilderness areas. Other sources of pollution, such as noise or motorized vehicle emissions, are known to impact the perceived quality of natural settings as well as the psychological well-being and satisfaction of visitors to those locations, but the effects of light pollution on visitors to natural settings is largely unstudied. Using experimental manipulations of light pollution levels in virtual reality simulations of three U.S. National Parks, the current study aimed to provide initial evidence of an effect on visitors. Results show that light pollution impacts a range of psychological and scene evaluation dimensions but that pristine night skies are not necessarily viewed as the ideal, likely due to being viewed as unfamiliar or unrealistic because so few have experienced the true baseline.

Light sources attract nocturnal flying insects, but some lamps attract more insects than others. The relation between the properties of a light source and the number of attracted insects is, however, poorly understood. We developed a model to quantify the attractiveness of light sources based on the spectral output. This model is fitted using data from field experiments that compare a large number of different light sources. We validated this model using two additional datasets, one for all insects and one excluding the numerous Diptera. Our model facilitates the development and application of light sources that attract fewer insects without the need for extensive field tests and it can be used to correct for spectral composition when formulating hypotheses on the ecological impact of artificial light. In addition, we present a tool allowing the conversion of the spectral output of light sources to their relative insect attraction based on this model.

Artificial light at night (ALAN) provides a unique footprint of human activities and settlements. However, the adverse effects of ALAN on human health and ecosystems have not been well understood. Because of a lack of high resolution data, studies of ALAN in China have been confined to coarse resolution, and fine-scale details are missing. The fine details of ALAN are pertinent, because the highly dense population in Chinese cities has created a distinctive urban lighting pattern. In this paper, we introduced a new generation of high spatial resolution and multi-spectral night-time light imagery from the satellite JL1-3B. We examined its effectiveness for monitoring the spatial pattern and discriminating the types of artificial light based on a case study of Hangzhou, China. Specifically, local Moran's I analysis was applied to identify artificial light hotspots. Then, we analyzed the relationship between artificial light brightness and land uses at the parcel-level, which were generated from GF-2 imagery and open social datasets. Third, a machine learning based method was proposed to discriminate the type of lighting sources – between high pressure sodium lamps (HPS) and light-emitting diode lamps (LED) – by incorporating their spectral information and morphology feature. The result shows a complicated heterogeneity of illumination characteristics across different land uses, where main roads, commercial and institutional areas were brightly lit while residential area, industrial area and agricultural land were dark at night. It further shows that the proposed method was effective at separating light emitted by HPS and LED, with an overall accuracy and kappa coefficient of 83.86% and 0.67, respectively. This study demonstrates the effectiveness of JL1-3B and its superiority over previous night-time light data in detecting details of lighting objects and the nightscape pattern, and suggests that JL1-3B and alike could open up new opportunities for the advancement of night-time remote sensing.

Light pollution has emerged as a pervasive component of land development over the past century. Several detrimental impacts of this anthropogenic influence have been identified in night shift workers, laboratory rodents, and a plethora of wildlife species. Circadian, or daily, patterns are interrupted by the presence of light at night and have the capacity to alter rhythmic physiological or behavioral characteristics. Indeed, biorhythm disruption can lead to metabolic, reproductive, and immunological dysfunction depending on the intensity, timing, duration and wavelength of light exposure. Light pollution, in many forms and by many pathways, is thus apt to affect the nature of host-pathogen interactions. However, no research has yet investigated this possibility. The goal of this manuscript is to outline how dim light at night (dLAN), a relevant and common form of light pollution, may affect disease dynamics by interrupting circadian rhythms and regulation of immune responses as well as opportunities for host-parasite interactions and subsequent transmission risk including spillover into humans. We close by proposing some promising interventions including alternative lighting methods or vector control efforts.